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Background: Accurately differentiating dementia subtypes, such as Alzheimer’s disease (AD) and Lewy body disease [including dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD)] is important to ensure appropriate management and treatment of the disease. Similarities in clinical presentation create difficulties for differential diagnosis. Simple supportive markers, such as balance assessments, may be useful to the diagnostic toolkit. This study aimed to identify differences in balance impairments between different dementia disease subtypes and normal aging using a single triaxial accelerometer. Methods: Ninety-seven participants were recruited, forming four groups: cognitive impairment due to Alzheimer’s disease (AD group; n = 31), dementia with Lewy bodies (DLB group; n = 26), Parkinson’s disease dementia (PDD group; n = 13), and normal aging controls (n = 27). Participants were asked to stand still for 2 minutes in a standardized position with their eyes open while wearing a single triaxial accelerometer on their lower back. Seven balance characteristics were derived, including jerk (combined, mediolateral, and anterior–posterior), root mean square (RMS; combined, mediolateral, and anterior–posterior), and ellipsis. Mann–Whitney U tests identified the balance differences between groups. Receiver operating characteristics and area under the curve (AUC) determined the overall accuracy of the selected balance characteristics. Results: The PDD group demonstrated higher RMS [combined (p = 0.001), mediolateral (p = 0.005), and anterior–posterior (p = 0.001)] and ellipsis scores (p < 0.002) than the AD group (AUC = 0.71–0.82). The PDD group also demonstrated significantly impaired balance across all characteristics (p ≤ 0.001) compared to the controls (AUC = 0.79–0.83). Balance differences were not significant between PDD and DLB (AUC = 0.69–0.74), DLB and AD (AUC = 0.50–0.65), DLB and controls (AUC = 0.62–0.68), or AD and controls (AUC = 0.55–0.67) foll

Background There are unique signatures of gait impairments in different dementia disease subtypes, such as Alzheimer’s disease (AD), dementia with Lewy bodies (DLB) and Parkinson’s disease (PDD). This suggests gait analysis is a useful differential marker for dementia disease subtypes, but this has yet to be assessed using inexpensive wearable technology. Research Question This study aimed to assess whether a single accelerometer-based wearable could differentiate dementia disease subtypes through gait analysis. Methods 80 people with mild cognitive impairment or dementia due to AD, DLB or PD performed six ten-metre walks. An accelerometer-based wearable (Axivity) assessed gait. Data was processed using algorithms validated in other neurological disorders and older adults. Fourteen spatiotemporal characteristic were computed, that broadly represent pace, variability, rhythm, asymmetry and postural control features of gait. One way analysis of variance and Kruskall Wallis tests identified significant between-group differences, and post-hoc independent t-tests and Mann Whitney U’s established where differences lay. Receiver Operating Characteristics and Area Under the Curve (AUC) demonstrated overall accuracy for single gait characteristics. Results The wearable was able to differentiate dementia disease subtypes (p ≤ .05) and demonstrated significant differences between the groups in 7 gait characteristics with modest accuracy. For reference the instrumented walkway showed 2 between-group differences in gait characteristics. Significance This study found that a wearable device can be used to differentiate dementia disease subtypes. This provides a foundation for future research to investigate the application of wearable technology as a clinical tool to aid diagnostic accuracy, allowing the correct treatment and care to be applied. Wearable technology may be particularly useful as its use is less restricted to context, making it easier to implement.

Parkinson’s disease (PD) is a common neurodegenerative disorder resulting in a range of mobility deficits affecting gait, balance and turning. In this paper, we present: (i) the development and validation of an algorithm to detect turns during gait; (ii) a method to extract turn characteristics; and (iii) the classification of PD using turn characteristics. Thirty-seven people with PD and 56 controls performed 180-degree turns during an intermittent walking task. Inertial measurement units were attached to the head, neck, lower back and ankles. A turning detection algorithm was developed and validated by two raters using video data. Spatiotemporal and signal-based characteristics were extracted and used for PD classification. There was excellent absolute agreement between the rater and the algorithm for identifying turn start and end (ICC ≥ 0.99). Classification modeling (partial least square discriminant analysis (PLS-DA)) gave the best accuracy of 97.85% when trained on upper body and ankle data. Balanced sensitivity (97%) and specificity (96.43%) were achieved using turning characteristics from the neck, lower back and ankles. Turning characteristics, in particular angular velocity, duration, number of steps, jerk and root mean square distinguished mild-moderate PD from controls accurately and warrant future examination as a marker of mobility impairment and fall risk in PD.

Background Physical activity is linked to many positive health outcomes, stimulating the development of exercise programs. However, many falls occur while walking and so promoting activity might paradoxically increase fall rates, causing injuries, and worse quality of life. The relationship between activity exposure and fall rates remains unclear. We investigated the relationship between walking activity (exposure to risk) and fall rates before and after an exercise program (V-TIME). Methods One hundred and nine older fallers, 38 fallers with mild cognitive impairment (MCI), and 128 fallers with Parkinson’s disease (PD) were randomly assigned to one of two active interventions: treadmill training only or treadmill training combined with a virtual reality component. Participants were tested before and after the interventions. Free-living walking activity was characterized by volume, pattern, and variability of ambulatory bouts using an accelerometer positioned on the lower back for 1 week. To evaluate that relationship between fall risk and activity, a normalized index was determined expressing fall rates relative to activity exposure (FRA index), with higher scores indicating a higher risk of falls per steps taken. Results At baseline, the FRA index was higher for fallers with PD compared to those with MCI and older fallers. Walking activity did not change after the intervention for the groups but the FRA index decreased significantly for all groups (p ≤ .035). Conclusions This work showed that V-TIME interventions reduced falls risk without concurrent change in walking activity. We recommend using the FRA index in future fall prevention studies to better understand the nature of intervention programs.

Wearable technology allows an in-depth analysis of gait behaviour in free-living environments. This investigation aimed to use Alzheimer's disease as an example to apply the time series analysis technique of Statistical Parametric Mapping (SPM) to create daily gait profiles and test if they differed from cognitively intact controls. A framework of macro (habitual walking behaviours) and micro characteristics (spatiotemporal gait variables) characteristics were calculated on an hourly basis. SPM showed that select micro gait characteristics differed from controls at specific hours of the day. Therefore, the application of SPM may provide a more in-depth reflection of activity and gait time-dependent fluctuations than commonly used whole day values. Considering macro and micro gait hour-by- hour may have applications towards disease management, personalized care, monitoring medication and targeted interventions for people with a range of neurodegenerative diseases.

We thank Dommershuijsen et al for their valuable comments on our recent article...

Background Gait is a potentially useful clinical tool to aid early diagnosis of dementia. Unique signatures of gait have been shown to discriminate dementia subtypes, such as Alzheimer's disease (AD) and Lewy body disease (LBD; including dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD)), from each other and normal ageing when assessed using “gold-standard” methodology, such as instrumented walkways. Body-worn monitors, such as tri-axial accelerometers, may allow inexpensive measurement of gait in clinical environments, and provide a more ecologically-valid picture of gait in free-living environments, i.e. home and community. The aim of this study was to explore the impact of the environment on gait characteristics for distinguishing AD and LBD from each other and controls. Methods 99 participants were recruited; 32 AD ((mean±SD) Age:77±6; MMSE:23±4), 28 DLB (Age:76±6; MMSE:24±4), 14 PDD (Age:78±6; MMSE:24±4) and 25 controls (Age:74±9; MMSE:29±1). Dementia subtypes ranged from mild cognitive impairment to moderate dementia. A tri-axial accelerometer (Axivity AX3) recorded gait data over six 10-metre walks in the lab, and continuously over seven days in free-living environments. One-way ANOVAs and non-parametric equivalents assessed group differences. Results Preliminary results show that all disease groups had impaired gait compared to controls (p≤.05) in both lab and free-living environments. In the lab, the PDD group demonstrated greater variability and asymmetry of gait (p≤.05) compared to AD and DLB, and in free-living by shorter steps and greater variability of gait (p≤.05). There were no differences between AD and DLB in the lab, however, people with AD had a significantly more asymmetric step length than DLB (p=.017, AUC=.677) in free-living environments. Conclusions This study is the first to provide evidence that gait can distinguish different disease subtypes from normal ageing using inexpensive body-worn monitors. It also highlights t

Objective Quantification of gait with wearable technology is promising; recent cross-sectional studies showed that gait characteristics are potential prodromal markers for Parkinson disease (PD). The aim of this longitudinal prospective observational study was to establish gait impairments and trajectories in the prodromal phase of PD, identifying which gait characteristics are potentially early diagnostic markers of PD. Methods The 696 healthy controls (mean age = 63 ± 7 years) recruited in the Tubingen Evaluation of Risk Factors for Early Detection of Neurodegeneration study were included. Assessments were performed longitudinally 4 times at 2-year intervals, and people who converted to PD were identified. Participants were asked to walk at different speeds under single and dual tasking, with a wearable device placed on the lower back; 14 validated clinically relevant gait characteristics were quantified. Cox regression was used to examine whether gait at first visit could predict time to PD conversion after controlling for age and sex. Random effects linear mixed models (RELMs) were used to establish longitudinal trajectories of gait and model the latency between impaired gait and PD diagnosis. Results Sixteen participants were diagnosed with PD on average 4.5 years after first visit (converters; PDC). Higher step time variability and asymmetry of all gait characteristics were associated with a shorter time to PD diagnosis. RELMs indicated that gait (lower pace) deviates from that of non-PDC approximately 4 years prior to diagnosis. Interpretation Together with other prodromal markers, quantitative gait characteristics can play an important role in identifying prodromal PD and progression within this phase. ANN NEUROL 2019;86:357–367

Early diagnosis of Parkinson’s diseases (PD) is challenging; applying machine learning (ML) models to gait characteristics may support the classification process. Comparing performance of ML models used in various studies can be problematic due to different walking protocols and gait assessment systems. The objective of this study was to compare the impact of walking protocols and gait assessment systems on the performance of a support vector machine (SVM) and random forest (RF) for classification of PD. 93 PD and 103 controls performed two walking protocols at their normal pace: (i) four times along a 10 m walkway (intermittent walk-IW), (ii) walking for 2 minutes on a 25 m oval circuit (continuous walk-CW). 14 gait characteristics were extracted from two different systems (an instrumented walkway—GAITRite; and an accelerometer attached at the lower back—Axivity). SVM and RF were trained on normalized data (accounting for step velocity, gender, age and BMI) and evaluated using 10-fold cross validation with area under the curve (AUC). Overall performance was higher for both systems during CW compared to IW. SVM performed better than RF. With SVM, during CW Axivity significantly outperformed GAITRite (AUC: 87.83 ± 7.81% vs. 80.49 ± 9.85%); during IW systems performed similarly. These findings suggest that choice of testing protocol and sensing system may have a direct impact on ML PD classification results and highlight the need for standardization for wide scale implementation.

The ability to perceive differences in environmental contrast is critical for navigating complex environments safely. People with Parkinson's disease (PD) report a multitude of visual and cognitive deficits which may impede safe obstacle negotiation and increase fall risk. Enhancing obstacle contrast may influence the content of visual information acquired within complex environments and thus target environmental fall risk factors. 17 PD with a history of falls and 18 controls walked over an obstacle covered in a high and low contrast material in separate trials whilst eye movements were recorded. Measures of visual function and cognition were obtained. Gaze location was extracted during the approach phase. PD spent longer looking at the obstacle compared to controls regardless of contrast (p < .05), however group differences were largest for the low contrast obstacle. When accounting for group differences in approach time, PD spent longer looking at the low contrast obstacle and less time looking at the ground beyond the low contrast obstacle compared to controls (p < .05). The response to obstacle contrast in PD (high-low) was significantly associated with executive function. Better executive function was associated with spending longer looking at the low contrast obstacle and at the ground beyond the high contrast obstacle. Enhancing the contrast of ground-based trip hazards may improve visual processing of environmental cues in PD, particularly for individuals with better executive function. Manipulating contrast to attract visual attention is already in use in the public domain, however its utility for reducing fall risk in PD is yet to be formally tested in habitual settings.

Wearable technology for the automatic detection of gait events has recently gained growing interest, enabling advanced analyses that were previously limited to specialist centres and equipment (e.g., instrumented walkway). In this study, we present a novel method based on dilated convolutions for an accurate detection of gait events (initial and final foot contacts) from wearable inertial sensors. A rich dataset has been used to validate the method, featuring 71 people with Parkinson's disease (PD) and 67 healthy control subjects. Multiple sensors have been considered, one located on the fifth lumbar vertebrae and two on the ankles. The aims of this study were: (i) to apply deep learning (DL) techniques on wearable sensor data for gait segmentation and quantification in older adults and in people with PD; (ii) to validate the proposed technique for measuring gait against traditional gold standard laboratory reference and a widely used algorithm based on wavelet transforms (WT); (iii) to assess the performance of DL methods in assessing high-level gait characteristics, with focus on stride, stance and swing related features. The results showed a high reliability of the proposed approach, which achieves temporal errors considerably smaller than WT, in particular for the detection of final contacts, with an inter-quartile range below 70 ms in the worst case. This study showes encouraging results, and paves the road for further research, addressing the effectiveness and the generalization of data-driven learning systems for accurate event detection in challenging conditions.

Objective: This longitudinal prospective observational study investigated if gait can predict Parkinson’s disease (PD) conversion from a cohort of community-dwelling older adults. Background: PD is a progressive disorder including a prodromal period when definitive motor/non-motor symptoms to permit a diagnosis have not yet appeared. Quantification of gait with wearable technology (WT) may serve as an accurate tool to identify surrogate markers of incipient disease manifestation. Recently arm swing and selective gait characteristics measured with WT have been shown to be potential prodromal markers for people at risk for PD [1]; however these data were obtained from a cross-sectional assessment; the potential of gait to predict PD conversion has not been investigated yet in a longitudinal cohort. Methods: 16 participants (69±5 years (yrs)) who were diagnosed with PD on average 4.5 yrs after baseline assessment (converters (PDC)) and 48 age-matched old healthy adults (HA) recruited in the TREND study were included. Assessments were performed longitudinally 4 times at 2-year intervals. Participants were asked to walk at their preferred speed, performing 2 straight-line trials over 20m with a WT device placed on the lower back; 14 validated clinically relevant gait characteristics were quantified [2]. ANCOVA was used to examine gait between-group differences; the value of baseline gait in predicting PDC was explored using AUC and stepwise, forward, logistic regression analyses. Random effects linear mixed-models (RELM) were used to predict latency gait deterioration and diagnosis of PD. Results: PDC walked with significantly lower pace, higher variability and asymmetry than HA (p≤0.027). Pace, variability and asymmetry characteristics were able to significantly predict PDC (AUC≥0.695). Step time variability was the best predictor for the stepwise, forward, logistic regression (sensitivity 25%, specificity 98%, accuracy of 80%). RELMs indicate gait impairment (step velo

[Poster] BACKGROUND AND AIM: Gait impairments are frequent among older adults and associated with fall risk. Intervention programmes aiming to reduce fall risk (e.g. balance exercise programs) usually focus on single risk factors (i.e. either motor or cognitive performance). The effects of interventions on free-living walking activity are still not clear and need to be explored. Recently, the V-TIME study showed that a six week multimodal intervention programme of treadmill training combined with a virtual reality component (TTVR) lowered the incidence of falls more than an intensity-matched intervention with treadmill training (TT) only [1]. The aim of this exploratory analysis was to examine the hypothesis that a lower fall risk due to the TTVR intervention would be mediated by change in volume, pattern and variability (macro gait outcomes) of free living walking activity. METHODS: 165 older adults (age: 74±7 years) including: 72 elderly fallers (EF), 24 people with mild cognitive impairment (MCI) and 69 people with Parkinson's disease (PD), who had fallen twice or more in the previous 6 months were assessed. Participants were randomly assigned to TT or TTVR interventions and tested at baseline and after the intervention (1 week, 1 month and 6 months) [1]. For each assessment free-living data were recorded for 7 days with an accelerometer (Axivity AX3) placed on the lower back. Macro gait outcomes representing the volume (% walking time, number of bouts per day, number of steps, mean bout length), pattern (alpha), and variability of free-living walking activity were extracted in MATLAB® (R2012a) [2]. General linear models were used to examine the effect of Group (EF vs PD vs MCI), Time and Intervention on macro gait, controlling for age and sex. RESULTS: Macro gait outcomes did not changed over time (main effect for Time p > 0.05). In addition, there were no significant Group x Time or Intervention x Time interactions. This suggests the lack of change was consiste

Background Falls are associated with gait impairments in older adults (OA) and Parkinson’s disease (PD). Current approaches for evaluating falls risk are based on self-report or one-time assessment and may be suboptimal. Wearable technology allows gait to be measured continuously in free-living conditions. The aim of this study was to explore generic and specific associations in free-living gait in fallers and nonfallers with and without PD. Methods Two hundred and seventy-seven fallers (155 PD, 122 OA) who fell twice or more in the previous 6 months and 65 nonfallers (15 PD, 50 OA) were tested. Free-living gait was characterized as the volume, pattern, and variability of ambulatory bouts (Macro), and 14 discrete gait characteristics (Micro). Macro and Micro variables were quantified from free-living data collected using an accelerometer positioned on the low back for one week. Results Macro variables showed that fallers walked with shorter and less variable ambulatory bouts than nonfallers, independent of pathology. Micro variables within ambulatory bouts showed fallers walked with slower, shorter and less variable steps than nonfallers. Significant interactions showed disease specific differences in variability with PD fallers demonstrating greater variability (step length) and OA fallers less variability (step velocity) than their nonfaller counterparts (p < 0.004). Conclusions Common and disease-specific changes in free-living Macro and Micro gait highlight generic and selective targets for intervention depending on type of faller (OA-PD). Our findings support free-living monitoring to enhance assessment. Future work is needed to confirm the optimal battery of measures, sensitivity to change and value for fall prediction.

Wearable technology is readily available for continuous assessment due to a growing number of commercial devices with increased data capture capabilities. However, many commercial devices fail to support suitable parameters (cut points) derived from the literature to help quantify physical activity (PA) due to differences in manufacturing. A simple metric to estimate cut points for new wearables is needed to aid data analysis. Objective The purpose of this pilot study was to investigate a simple methodology to determine cut points based on ratios between sedentary behaviour (SB) and PA intensities for a new wrist worn device (PRO-Diary™) by comparing its output to a validated and well characterised ‘gold standard’ (ActiGraph™). Study design Twelve participants completed a semi-structured (four-phase) treadmill protocol encompassing SB and three PA intensity levels (light, moderate, vigorous). The outputs of the devices were compared accounting for relative intensity. Results Count ratios (6.31, 7.68, 4.63, 3.96) were calculated to successfully determine cut-points for the new wrist worn wearable technology during SB (0–426) as well as light (427–803), moderate (804–2085) and vigorous (≥2086) activities, respectively. Conclusion Our findings should be utilised as a primary reference for investigations seeking to use new (wrist worn) wearable technology similar to that used here (i.e., PRO-Diary™) for the purposes of quantifying SB and PA intensities. The utility of count ratios may be useful in comparing devices or SB/PA values estimated across different studies. However, a more robust examination is required for different devices, attachment locations and on larger/diverse cohorts.

Contains fulltext : 167962.pdf (publisher's version ) (Closed access), BACKGROUND: Age-associated motor and cognitive deficits increase the risk of falls, a major cause of morbidity and mortality. Because of the significant ramifications of falls, many interventions have been proposed, but few have aimed to prevent falls via an integrated approach targeting both motor and cognitive function. We aimed to test the hypothesis that an intervention combining treadmill training with non-immersive virtual reality (VR) to target both cognitive aspects of safe ambulation and mobility would lead to fewer falls than would treadmill training alone. METHODS: We carried out this randomised controlled trial at five clinical centres across five countries (Belgium, Israel, Italy, the Netherlands, and the UK). Adults aged 60-90 years with a high risk of falls based on a history of two or more falls in the 6 months before the study and with varied motor and cognitive deficits were randomly assigned by use of computer-based allocation to receive 6 weeks of either treadmill training plus VR or treadmill training alone. Randomisation was stratified by subgroups of patients (those with a history of idiopathic falls, those with mild cognitive impairment, and those with Parkinson's disease) and sex, with stratification per clinical site. Group allocation was done by a third party not involved in onsite study procedures. Both groups aimed to train three times per week for 6 weeks, with each session lasting about 45 min and structured training progression individualised to the participant's level of performance. The VR system consisted of a motion-capture camera and a computer-generated simulation projected on to a large screen, which was specifically designed to reduce fall risk in older adults by including real-life challenges such as obstacles, multiple pathways, and distracters that required continual adjustment of steps. The primary outcome was the incident rate of falls during the 6 months after the end of training, which was assessed in a modified intenti

Background Gait is emerging as a powerful diagnostic and prognostic tool, and as a surrogate marker of disease progression for Parkinson’s disease (PD). Accelerometer-based body worn monitors (BWMs) facilitate the measurement of gait in clinical environments. Moreover they have the potential to provide a more accurate reflection of gait in the home during habitual behaviours. Emerging research suggests that measurement of gait using BWMs is feasible but this has not been investigated in depth. The aims of this study were to explore (i) the impact of environment and (ii) ambulatory bout (AB) length on gait characteristics for discriminating between people with PD and age-matched controls. Methods Fourteen clinically relevant gait characteristics organised in five domains (pace, variability, rhythm, asymmetry, postural control) were quantified using laboratory based and free-living data collected over 7 days using a BWM placed on the lower back in 47 PD participants and 50 controls. Results Free-living data showed that both groups walked with decreased pace and increased variability, rhythm and asymmetry compared to walking in the laboratory setting. Four of the 14 gait characteristics measured in free-living conditions were significantly different between controls and people with PD compared to two measured in the laboratory. Between group differences depended on bout length and were more apparent during longer ABs. ABs ≤ 10s did not discriminate between groups. Medium to long ABs highlighted between-group significant differences for pace, rhythm and asymmetry. Longer ABs should therefore be taken into account when evaluating gait characteristics in free-living conditions. Conclusion This study provides encouraging results to support the use of a single BWM for free-living gait evaluation in people with PD with potential for research and clinical application.

Biomarkers are required to track disease progression and measure the effectiveness of interventions for people with spinocerebellar ataxia type-6 (SCA6). Gait is a potential biomarker that is sensitive to SCA6 which can be measured using wearable technology, reducing the need for expensive specialist facilities. However, algorithms used to calculate gait using data from wearables have not been validated in SCA6. This study sought to examine the validity of a single wearable for deriving 14 spatio-temporal gait characteristics in SCA6 and control cohorts. Participants performed eight intermittent walks along a 7 m instrumented walkway at their preferred walking pace while also wearing a single accelerometer-based wearable on L5. Gait algorithms previously validated in neurological populations and controls were used to derive gait characteristics. We assessed the bias, agreement and sensitivity of gait characteristics derived using the instrumented walkway and the wearable. Mean gait characteristics showed good to excellent agreement for both groups, although gait variability and asymmetry showed poor agreement between the two systems. Agreement improved considerably in the SCA6 group when people who used walking sticks were excluded from the analysis, suggesting poorer agreement in people with more severe gait impairment. Despite poor agreement for some characteristics, gait measured using the wearable was generally more sensitive to group differences than the instrumented walkway. Our findings indicate mean gait characteristics can be accurately measured using an accelerometer-based wearable in people SCA6 with mild-to-moderately severe gait impairment yet further development of algorithms are required for people with more severe symptoms.

Impaired postural control (PC) is an important feature of Parkinson’s disease (PD), but optimal testing protocols are yet to be established. Accelerometer-based monitors provide objective measures of PC. We characterised time-dependent changes in PC in people with PD and controls during standing, and identified outcomes most sensitive to pathology. Thirty-one controls and 26 PD patients were recruited: PC was measured with an accelerometer on the lower back for 2 minutes (mins). Preliminary analysis (autocorrelation) that showed 2 seconds (s) was the shortest duration sensitive to changes in the signal; time series analysis of a range of PC outcomes was undertaken using consecutive 2-s windows over the test. Piecewise linear regression was used to fit the time series data during the first 30 s and the subsequent 90 s of the trial. PC outcomes changed over the 2 mins, with the greatest change observed during the first 30 s after which PC stabilised. Changes in PC were reduced in PD compared to controls, and Jerk was found to be discriminative of pathology. Previous studies focusing on average performance over the duration of a test may miss time-dependent differences. Evaluation of time-dependent change may provide useful insights into PC in PD and effectiveness of intervention.

Gait is a powerful tool to identify ageing and track disease progression. Yet, its measurement via traditional instrumentations remains restricted to the laboratory or bespoke clinical facilities. The potential for that to change is due to the advances in wearable technology where the synergy between accelerometer-based body worn monitors and smart algorithms has provided the potential of ‘a gait lab on a chip’. The deployment of wearables can allow the researcher/clinician to continuously assess the participant accurately and robustly over time. Commercially available wearables for gait quantification remain expensive and are restricted to a limited number of characteristics unsuitable for a comprehensive clinical assessment. However, the increasing demand for low cost diagnostics has fuelled the shift in how health-related resources are distributed. As such the interest in open platform technology and novel research methodologies has begun to harmonise engineering solutions with clinical needs. We provide an introduction to conduct an instrumented gait assessment with a discrete, low cost, accelerometer-based body worn monitor. We show that the capture and interpretation of raw gait signals with a common scripting language (MATLAB®) can be straightforward. In addition, we highlight best approaches and hope that this will help compliment any analytical tool-kit as part of any modern clinical assessment.

Wearable technology is readily available for continuous assessment due to a growing number of commercial devices with increased data capture capabilities. However, many commercial devices fail to support suitable parameters (cut points) derived from the literature to help quantify physical activity (PA) due to differences in manufacturing. A simple metric to estimate cut points for new wearables is needed to aid data analysis. Objective: The purpose of this pilot study was to investigate a simple methodology to determine cut points based on ratios between sedentary behaviour (SB) and PA intensities for a new wrist worn device (PRO-Diary™) by comparing its output to a validated and well characterised ‘gold standard’ (ActiGraph™). Study design: Twelve participants completed a semi-structured (four-phase) treadmill protocol encompassing SB and three PA intensity levels (light, moderate, vigorous). The outputs of the devices were compared accounting for relative intensity. Results: Count ratios (6.31, 7.68, 4.63, 3.96) were calculated to successfully determine cut-points for the new wrist worn wearable technology during SB (0–426) as well as light (427–803), moderate (804–2085) and vigorous (≥2086) activities, respectively. Conclusion: Our findings should be utilised as a primary reference for investigations seeking to use new (wrist worn) wearable technology similar to that used here (i.e., PRO-Diary™) for the purposes of quantifying SB and PA intensities. The utility of count ratios may be useful in comparing devices or SB/PA values estimated across different studies. However, a more robust examination is required for different devices, attachment locations and on larger/diverse cohorts.

[Poster] Objective: To determine whether impaired postural control is associated with cholinergic dysfunction in early Parkinson's disease (PD). Background: Impaired postural control is present even in early PD, and is an important determinant of impaired mobility and falls risk. Postural impairment is underpinned by complex, multisystem pathophysiology. While basal ganglia pathology and associated dopaminergic denervation are key contributors, recent work also implicates cholinergic degeneration in impaired postural control. Methods: Short latency afferent inhibition (SAI), a proxy measure of cholinergic activity, and postural control were measured in 42 subjects with early PD (mean age 70.1 ± 9.9 years) and 38 age-matched controls (68.7 ± 8.4 years) as part of the ICICLE-PD study. SAI was determined by conditioning motor evoked potentials, elicited by transcranial magnetic stimulation of the motor cortex, with electrical stimuli delivered to the contralateral median nerve at intervals ranging from N20 (predetermined) to N20+4ms. Postural control was measured during two minutes of quiet standing with eyes open. Force plates were used to quantify sway, from which mean speed of the centre of pressure (CoP) in the anterior-posterior (AP) and medio-lateral (ML) direction were determined. Partial correlations, controlling for age and cognition, were used to explore relationships between SAI and postural control. Results: SAI was significantly reduced in PD participants compared to controls (76.7 + 28.8% vs. 58.5 + 22.4%), indicating greater cholinergic dysfunction. There was no difference in postural control outcomes between the groups. In PD but not control participants, increased speed of movement of the CoP in the AP and ML direction (impaired postural control) was significantly associated with reduced SAI (cholinergic dysfunction), and this remained significant after controlling for age and cognition (r=0.565, p=0.008; r=0.632, p=0.002, respectively). Conclusions: O